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????个人信条：格物致知。
The intelligent decision making systems are useful toolsfor the assistance of human expert, and or as a perfect alternativefor expert in a variety of auto-decision making fields. The use ofsuch systems in education, agriculture, industry, fishery, animalhusbandry etc., can decrease manpower errors or need of it; In theother hand, it can increase the quality and the pace of servicegiving. The interview at the PH.D level or even Master’s degree,due to the high sensitivity in scoring to the candidates, is of highimportance. Therefore, creating a system for storing these scores,
and inferring the results can be beneficial when there is a largenumber of candidates. In this paper, the expert system has aneducational use, and classifies the probability of acceptance orunacceptance of PH.D candidates in the exam and interview, basedon the (National Organization of Educational Testing) NOETmeasures, also estimates scientific level of candidates. Theproposed fuzzy-expert system takes advantage of the particleswarm optimization (PSO) evolutionary algorithm to specifying
the score of each variable, and eventually the final condition of the
candidate. The acquired results of evaluating the fuzzy-expertsystem proves its functionality. This system is also able to functionwell in scoring similar educational cases to specify acceptance.
? 部分代码
%Plot the whole view
plotfis(pso);
%Plot each mf(input/output)
figure
[x,mf] = plotmf(pso,’input’,1);
subplot(5,1,1), plot(x,mf);
xlabel(‘Membership Functions for Ex-uni Length’);
[x,mf] = plotmf(pso,’input’,2);
subplot(5,1,2), plot(x,mf);
xlabel(‘Membership Functions for Ex-uni Average’);
[x,mf] = plotmf(pso,’input’,3);
subplot(5,1,3), plot(x,mf);
xlabel(‘Membership Functions for Olympiad Chosen’);
[x,mf] = plotmf(pso,’input’,4);
subplot(5,1,4), plot(x,mf);
xlabel(‘Membership Functions for Ex-Uni Quality’);
[x,mf] = plotmf(pso,’input’,5);
subplot(5,1,5), plot(x,mf);
xlabel(‘Membership Functions for IELTS’);
figure
[x,mf] = plotmf(pso,’input’,6);
subplot(5,1,1), plot(x,mf);
xlabel(‘Membership Functions for Papers’);
[x,mf] = plotmf(pso,’input’,7);
subplot(5,1,2), plot(x,mf);
xlabel(‘Membership Functions for Festival Chosen’);
[x,mf] = plotmf(pso,’input’,8);
subplot(5,1,3), plot(x,mf);
xlabel(‘Membership Functions for Compilation,Translation,Invention’);
[x,mf] = plotmf(pso,’input’,9);
subplot(5,1,4), plot(x,mf);
xlabel(‘Membership Functions for Msc Thesis Quality’);
[x,mf] = plotmf(pso,’input’,10);
subplot(5,1,5), plot(x,mf);
xlabel(‘Membership Functions for Interviews Test’);
figure
[x,mf] = plotmf(pso,’output’,1);
subplot(2,1,1), plot(x,mf);
xlabel(‘Membership Functions for Qualification(output)’);
? 运行结果
? 参考文献
Mousavi, Seyed Muhammad Hossein, et al. “A PSO fuzzy-expert system: As an assistant for specifying the acceptance by NOET measures, at PH. D level.” 2017 Artificial Intelligence and Signal Processing Conference (AISP). IEEE, 2017.
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